Discrete Integration by Decoding Binary CodesDownload PDF

29 Mar 2024 (modified: 21 Apr 2013)ICML 2013 Inferning submissionReaders: Everyone
Decision: conferencePoster
Abstract: Many probabilistic inference and learning tasks involve summations over exponentially large sets. Recently, it has been shown that these problems can be reduced to solving a polynomial number of MAP inference queries for a model augmented with randomly generated parity constraints. By exploiting a connection with max-likelihood decoding of binary codes, we show that these optimizations are computationally hard. Inspired by iterative message passing decoding algorithms, we propose an Integer Linear Programming (ILP) formulation for the problem, enhanced with new sparsification techniques to improve decoding performance. By solving the ILP through a sequence of LP relaxations, we get both lower and upper bounds on the partition function, which hold with high probability and are much tighter than those obtained with variational methods.
2 Replies

Loading